Stroke is the leading cause of long term disability among adults in industrialized nations. The partial paralysis that stroke patients often experience can make independent living difficult or impossible. Research suggests that many of these patients could recover by performing hundreds of daily repetitions of motions with their affected limbs. Yet, only 31% of patients perform the exercises recommended by their therapists. Home-based stroke rehabilitation games may help motivate stroke patients to perform the necessary exercises to recover. In this paper, we describe a formative study in which we designed and user tested stroke rehabilitation games with both stroke patients and therapists. We describe the lessons we learned about what makes games useful from a therapeutic point of view.

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Stroke is the leading cause of long-term disability among adults in industrialized nations; approximately 80% of people who survive a stroke experience motor disabilities. Recovery requires hundreds of daily repetitions of therapeutic exercises, often without therapist supervision. When performing therapy alone, people with limited motion often compensate for the lack of motion in one joint by moving another one. This compensation can impede the recovery progress and create new health problems. In this work we contribute (1) a methodology to reliably sense compensatory torso motion in the context of shoulder exercises done by persons with stroke and (2) the design and experimental evaluation of operant-conditioning-based strategies for games that aim to reduce compensatory torso motion. Our results show that these strategies significantly reduce compensatory motions compared to alternatives.

In this paper, we present a smartphone platform for colorimetric water quality detection based on the use of built-in camera for capturing a single-use reference image. A custom-developed app processes this image for training and creates a reference model to be used later in real experimental conditions to calculate the concentration of the unknown solution. This platform has been tested on four different water quality colorimetric assays with various concentration levels, and results show that the presented platform provides approximately 100% accuracy for colorimetric assays with noticeable color difference. This portable, cost-effective, and user-friendly platform is promising for application in water quality monitoring.